806 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions in United Kingdom
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Audition for Robots (ActivATOR)” under the direction of Dr Christine Evers. The position will be in the Vision, Learning and Control (VLC) Group, which is part of the School of Electronics and Computer
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search using vacancy reference B02-09130. About you You will have a PhD in Computational Biology, Applied Mathematics, Statistics, Biostatistics, Epidemiology, Bioinformatics, Computer
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a PhD in Computational Biology, Applied Mathematics, Statistics, Biostatistics, Epidemiology, Bioinformatics, Computer Science, Neurological Genetics or a closely related discipline. Excellent oral
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funders. It is essential that you have a PhD in computer science, or equivalent professional qualifications and experience; ideally your PhD or equivalent professional qualifications and experience will be
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the boundaries of 6G technology. Your primary responsibility will be to spearhead research initiatives aimed at developing the domain of Edge Computing for 6G Networks. Holding a PhD (or have submitted your thesis
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fragmentation (MSⁿ) data and computational methods and approaches (e.g. spectral matching, network-based approaches, and machine learning techniques, etc). Design and apply robust statistical analysis strategies
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and